Showing papers by "Jens Schmidt published in 2019"

Myalgia in myositis and myopathies

Abstract: Myalgia is a common symptom of various neuromuscular disorders: myalgia occurs in metabolic muscle diseases, inflammatory muscle diseases, dystrophic myopathies and myotonic muscle disorders. Myalgia leads to a significantly reduced quality of life. Other muscular symptoms that are present along with myalgia often provide the clue towards a diagnosis and include weakness, cramps and myotonia as well as the type of pain. In addition, extramuscular symptoms like an erythema in dermatomyositis can lead to the correct diagnosis. Basic diagnostic workup includes a detailed medical history, full neurologic assessment, laboratory tests, EMG and nerve conduction studies. Muscle imaging, genetic testing and muscle biopsy may be required to make a diagnosis. Whenever possible, treatment should aim to improve or correct the underlying cause for myalgia such as inflammation or hypothyroidism. Symptomatic therapy includes different avenues: Myotonia can be treated with mexiletine. Carbamazepine or phenytoin can be used in myotonic syndromes, particularly with muscle cramps. Pregabalin, gabapentin, or amitriptyline can be tried in conditions with myalgic pain. This review summarizes the symptoms, diagnostic strategies, and therapeutic approach in neuromuscular disorders that present with myalgia.

X-linked myotubular myopathy and recurrent spontaneous pneumothorax: A new phenotype?

Abstract: X-linked myotubular myopathy (XLMTM) is a rare hereditary disorder of the skeletal muscle. Symptoms include impaired respiration and muscular hypotonia, usually present at birth and leading to death during infancy or early childhood.1 Pneumothorax, defined as trapping of air in the pleural cavity, can be caused by surgery or can occur spontaneously.2 Pneumothorax has been reported only in a small number of cases with hereditary myopathies, but usually not spontaneously and never in XLMTM.

Development and validation of a Bayesian survival model for inclusion body myositis

Abstract: Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84–3.50) and 2.48 (95% CrI: 1.27–5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37–10.61). For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.